The effects of logical permutation in the 3-satisfiability reverse analysis method

• Main Authors: Malik, Amierah Abdul, Manoharam, Gaeithry
• Format: Conference Proceeding
• Language: English
• Subjects: Logic; Modernization; Neural networks; Permutations
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0224420

Logical permutation in 3-Satisfiability Reverse Analysis Method (P3-SATRA) is a modernized logic mining with a better logical rule arrangement. The primary purpose of logic mining is to identify beneficial insight from a set of data through a logical rule. A significant element in developing logic mining models would be the arrangement of variables within the logical rule. The fundamental problems with logic the conventional logic mining approaches, 3-Satisfiability Reverse Analysis Method incorporated (3-SATRA) with Hopfield Neural Network (HNN) are a shortage of dependability and inadequacies in the structure of 3-Satisfiability-based logic. This problem can be tackled by introducing a specific finite rearrangement 3-Satisfiability logical rule. Therefore, this work explores the effect of logical rearrangement known as permutation in logic mining by comparing it with the standard logic mining approach. Various types of multivariate datasets are employed to validate the implications of logical permutations. As compared to the 3-SATRA logic mining, the experimental observations convey an evolution in the newly proposed logical permutation logic mining technique regarding accuracy, precision, and sensitivity.